We propose to study the precision with which retinal circuits code different types of stimulus features. This will be important for understanding how retinal circuits inform the brain about tasks important for survival such as distinguishing a fleeing animal from one approaching. The retina's ability to signal the visual world is limited by biological mechanisms, because the retinal output to the brain is limited by saturation and noise. To cope with this problem, the retina removes the background level using a variety of adaptation mechanisms, allowing ganglion cell signals to code for fine details. However the inevitable cost of these mechanisms is the addition of synaptic noise to the signal which limits fine details'visibility. Although much is known about circuits presynaptic to ganglion cells, what is lacking beyond important details is an understanding of the rationale behind their signal processing mechanisms. For example, it is unknown how a ganglion cell's presynaptic circuit shapes its neural code, the pattern of response that can inform about a stimulus, nor is it known what signal processing tradeoffs make necessary such circuit mechanisms as pooling of the receptive field center signal by convergence and gap junction coupling, and receptive field surround subtraction from amacrine and horizontal cell feedback. Using in vitro live recordings from characterized ganglion cells and horizontal cells and realistic computational models of them, we propose to test several hypotheses about the role of circuits presynaptic to the ganglion cell in its sensitivity and neural code. We hypothesize that a ganglion cell can simultaneously distinguish several stimuli that differ in contrast, size, or location, because these stimuli are conveyed by different neural codes. We will analyze the neural responses with an ideal observer, a computer program that discriminates using the likelihood rule between the responses to a pair of stimuli in a behaviorally-relevant task to measure the precision with which a neuron signals e.g. contrast or motion, and to measure the neural code. Using the ideal observer to analyze single and multiple recordings from retinal neurons, we will determine the sensitivity and neural code for discriminating multiple visual features. Next, we hypothesize that the retinal signal that relays the background level modulates the maintained synaptic release rate and signal-to-noise ratio (SNR) of the receptive field center, and that these are also modulated by the surround. Using live recordings and models, we will study how the ganglion cell's presynaptic circuitry for center and surround control its SNR. Last, we hypothesize that reciprocal inhibition between starburst amacrine cells generates positive feedback to amplify the directional signal for the direction-selective ganglion cell. Using live recordings and models, we will test the hypothesis that reciprocal synaptic feedback helps the starburst amacrine network maximize sensitivity to direction of motion and reduce noise in the direction- selective ganglion cell. These proposed studies are new and important and will provide knowledge about circuit function relevant to a basic understanding of the brain, its behavior, and clinical testing of disease. PUBLIC HEALTH RELEVANCE: The proposed studies of retinal circuitry will provide new knowledge about how the retina functions to reliably detect features of the visual environment. The use of ideal observer analysis allows comparing the performance of one or several neurons to the performance of a person looking at the same stimulus. The results provided by this method are relevant to public health because it will help scientists and eye doctors to determine the neural circuits that are responsible for vision in health and disease.